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An Attention-Locating Algorithm for Eliminating Background Effects in Fine-grained Visual Classification

IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5993-6006页

作者： Huang, Yueting Hechen, Zhenzhe Zhou, Mingliang Li, Zhengguo Kwong, Sam Chongqing University School of Computer Science Chongqing400044 China Agency for Science Technology and Research Institute for Infocomm Research Singapore138632 Singapore Lingnan University Department of Computing and Decision Science Hong Kong

Fine-grained visual classification (FGVC) is a challenging task characterized by interclass similarity and intraclass diversity and has broad application prospects. Recently, several methods have adopted the vision Transformer (ViT) in FGVC tasks since the data specificity of the multihead self-attention (MSA) mechanism in ViT is beneficial for extracting discriminative feature representations. However, these works focus on integrating feature dependencies at a high level, which leads to the model being easily disturbed by low-level background information. To address this issue, we propose a fine-grained attention-locating vision Transformer (FAL-ViT) and an attention selection module (ASM). First, FAL-ViT contains a two-stage framework to identify crucial regions effectively within images and enhance features by strategically reusing parameters. Second, the ASM accurately locates important target regions via the natural scores of the MSA, extracting finer low-level features to offer more comprehensive information through position mapping. Extensive experiments on public datasets demonstrate that FAL-ViT outperforms the other methods in terms of performance, confirming the effectiveness of our proposed methods. The source code is available at https://***/Yueting-Huang/FAL-ViT. © 1991-2012 IEEE.

关键词： Feature extraction Transformers Visualization Data mining computer vision Circuits and systems Training Computational modeling Classification algorithms Background noise

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Characterization of Exact One-Query Quantum Algorithms for Partial Boolean Functions

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Journal of computer science & Technology 2023年第6期38卷 1423-1430页

作者：叶泽坤李绿周 Institute of Quantum Computing and Computer Theory School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510006China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen UniversityGuangzhou 510006China

The query model(or black-box model)has attracted much attention from the communities of both classical and quantum ***,quantum advantages are revealed by presenting a quantum algorithm that has a better query complexity than its classical *** the history of quantum algorithms,the Deutsch algorithm and the Deutsch-Jozsa algorithm play a fundamental role and both are exact one-query quantum *** leads us to con-sider the problem:what functions can be computed by exact one-query quantum algorithms?This problem has been ad-dressed in the literature for total Boolean functions and symmetric partial Boolean functions,but is still open for general partial Boolean ***,in this paper,we continue to characterize the computational power of exact one-query quantum algorithms for general partial Boolean ***,we present several necessary and sufficient conditions for a partial Boolean function to be computed by exact one-query quantum ***,inspired by these conditions,we discover some new representative functions that can be computed by exact one-query quantum algorithms but have an essential difference from the already known ***,it is worth pointing out that before our work,the known func-tions that can be computed by exact one-query quantum algorithms are all symmetric functions and the quantum algo-rithm used is essentially the Deutsch-Jozsa algorithm,whereas the functions discovered in this paper are generally asym-metric and new algorithms to compute these functions are ***,this expands the class of functions that can be computed by exact one-query quantum algorithms.

关键词： quantum computing quantum query complexity quantum algorithm

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ESOA-Based 1D-CNN Classification for Amazon Textual Review Sentiment Analysis of Mobile Phones

ESOA-Based 1D-CNN Classification for Amazon Textual Review S...

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International Conference on computing and Machine Learning, CML 2024

作者： Kosala, N. Nirmalrani, V. School of Computing Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9789819775484

A trend toward the use of digital platforms for product marketing and profit margin enhancement is evident in the contemporary corporate environment. As a result, using deep learning methods to evaluate consumer sentiment in tweets based on product reviews becomes essential. The study's proposal is to use a big dataset and a one-dimensional convolutional neural network (1D-CNN) for the classification process in order to extract lexical characteristics for sentiment analysis. In order to do this, analysis is done on customer review datasets of Amazon items with an emphasis on reviews pertaining to mobile phones. To improve the performance and accuracy of the classifier, data preprocessing is first done. A lexical method is then used to efficiently compress the text via feature extraction. For the purpose of correctly assessing review emotions, efficient classification is essential. So, classification is done using the 1D-CNN method. The Egret Swarm Optimization Algorithm is used to adjust the 1D-CNN model's hyperparameters (ESOA). After this procedure, the research evaluates the 1D-CNN method's performance in relation to other techniques currently in use. The research concludes that the suggested method performs well overall, achieving 98.77% accuracy, 97.37% precision, 97.42% sensitivity, and 97.21% F-measure, respectively. These performance measures outperform those of current algorithms created in earlier research. The sentiment analysis of mobile phone evaluations inside the Amazon e-commerce system shows improved accuracy and efficiency when using the suggested 1D-CNN neural network approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Envisaging the patterns in Blockchain 7

Envisaging the patterns in Blockchain

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Nagarajan, G. Atmakuri, Sukeerth Nalamati, Sri Satyendra Minu, R.I. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350372816

Cryptocurrency is becoming more mainstream, and experts predict that its market size will reach dollar 2.2 trillion by 2026. However, it's also an intangible and highly volatile asset that poses significant challenges for governments, anti-money laundering analysts, and financial crime investigators. Compliance with strict regulations is also a challenge facing virtual asset service providers. The price of Bitcoin hit an all-time high in January 2021, making headlines worldwide. As the popularity of cryptocurrency continues to grow, AML analysts and financial crime investigators face tougher challenges. VASPs are under pressure to comply with tighter regulations, and both parties need to take a proactive approach. Bitcoin visualization is a solution that involves visualizing a Bitcoin block, enabling analysts to clarify complex and dense blocks of transaction data quickly to identify high-risk activity. Instead of looking at historic Bitcoin transactions, analysts can now examine Bitcoin activity in real-time using a powerful Bitcoin network visualization chart showing key connections, combined with a dedicated Bitcoin timeline view featuring precise transaction details as they occur. We conducted a thorough and quantitative assessment of 76 analytics tools, including those proposed in academic research and those endorsed by professionals and blockchain enthusiasts online, to provide a comprehensive analysis of the facets of blockchain data that have been thoroughly investigated. We also identified areas that warrant further exploration in future research. © 2024 IEEE.

关键词： Blockchain Blockchain tools Data parsing Data Visualization Graph Modelling Graph-based analysis

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Big Mart Sales Prediction Using Machine Learning 10

Big Mart Sales Prediction Using Machine Learning

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Mondal, Sagar Debbarma, Anindita Prakash, B. Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai India Srm Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350353068

In today's competitive retail landscape, supermarkets like Big Marts meticulously track the sales data of each product to anticipate consumer demand and optimize inventory management. By analyzing this data, including identifying anomalies and trends through data mining techniques, retailers can develop predictive models using advanced machine learning algorithms. Techniques such as Xgboost, Linear Regression, Polynomial Regression, and Ridge Regression are employed to forecast sales volumes with greater accuracy than traditional methods. Through the application of these models, businesses like Big Mart can make informed decisions to enhance their operational efficiency and strategic planning, ensuring they stay ahead in the dynamic market environment. © 2024 IEEE.

关键词： Sales

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge computing

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computer Modeling in Engineering & sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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Qualitative and Quantitative Model Checking Against Recurrent Neural Networks

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Journal of computer science & Technology 2024年第6期39卷 1292-1311页

作者： Zhen Liang Wan-Wei Liu Fu Song Bai Xue Wen-Jing Yang Ji Wang Zheng-Bin Pang Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Institute of Software Chinese Academy of SciencesBeijing 100190China

Recurrent neural networks (RNNs) have been heavily used in applications relying on sequence data such as time series and natural languages. As a matter of fact, their behaviors lack rigorous quality assurance due to the black-box nature of deep learning. It is an urgent and challenging task to formally reason about the behaviors of RNNs. To this end, we first present an extension of linear-time temporal logic to reason about properties with respect to RNNs, such as local robustness, reachability, and some temporal properties. Based on the proposed logic, we formalize the verification obligation as a Hoare-like triple, from both qualitative and quantitative perspectives. The former concerns whether all the outputs resulting from the inputs fulfilling the pre-condition satisfy the post-condition, whereas the latter is to compute the probability that the post-condition is satisfied on the premise that the inputs fulfill the pre-condition. To tackle these problems, we develop a systematic verification framework, mainly based on polyhedron propagation, dimension-preserving abstraction, and the Monte Carlo sampling. We also implement our algorithm with a prototype tool and conduct experiments to demonstrate its feasibility and efficiency.

关键词： recurrent neural network model checking temporal logic qualitative/quantitative verification

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Determination and Classification of Diabetes Mellitus Applying Ensemble Machine Learning Classifiers 1

Determination and Classification of Diabetes Mellitus Applyi...

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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Patel, Siddharth Hassan, Zayd Iniyan, S. SRM Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350361780

This paper addresses the accurate and early prediction of the onset and progression of diabetes, an important aspect in the management of this chronic metabolic disease given a variety of information like the history of the diseased person and his day-to-day routine factors by using state-of-the-art machine learning techniques to obtain predictive dynamic models. The proposed work not only demonstrates the effectiveness of different algorithms but also highlights the importance of feature engineering to refine predictions. This work uses ensemble classifiers and voting classifier algorithms to develop better accuracy for diabetes prediction. Combining the results of multiple base classifiers like KNN, SVM, RFC, and decision tree ensemble methods aims to enhance the predictive ability and chances of improvement in the patient. The treatment adopted is different from patient to patient based on the result of the treatment. In this research, we utilize a combination of ensemble techniques and a voting classifier to construct a comprehensive diabetes prediction model. This model is evaluated using real-world data. Our findings reveal the exceptional effectiveness of ensemble classifiers and the voting classifier, underscoring their potential to achieve precision and the possibility of recovery and probability. These outcomes not only enable early intervention but also offer valuable insights into the care of individual patients. © 2024 IEEE.

关键词： Decision trees

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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